Co-learning analysis of two perceptual learning tasks with identical input stimuli supports the reweighting hypothesis

被引:19
|
作者
Huang, Chang-Bing [3 ,4 ]
Lu, Zhong-Lin [1 ,3 ]
Dosher, Barbara A. [2 ]
机构
[1] Ohio State Univ, Dept Psychol, Lab Brain Proc LOBES, Columbus, OH 43210 USA
[2] Univ Calif Irvine, Dept Cognit Sci, Irvine, CA 92697 USA
[3] Univ So Calif, Dept Psychol, Lab Brain Proc LOBES, Los Angeles, CA 90089 USA
[4] Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing 100101, Peoples R China
关键词
Perceptual learning; Vernier; Bisection; Representation enhancement; Selective reweighting; PRIMARY VISUAL-CORTEX; EXTERNAL NOISE; MECHANISMS; SPECIFICITY; ORIENTATION; INCREASES; RESPONSES; DYNAMICS; MODELS; V1;
D O I
10.1016/j.visres.2011.11.003
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Perceptual learning, even when it exhibits significant specificity to basic stimulus features such as retinal location or spatial frequency, may cause discrimination performance to improve either through enhancement of early sensory representations or through selective re-weighting of connections from the sensory representations to specific responses, or both. For most experiments in the literature, the two forms of plasticity make similar predictions (Dosher & Lu, 2009; Petrov, Dosher, & Lu, 2005). The strongest test of the two hypotheses must use training and transfer tasks that rely on the same sensory representation with different task-dependent decision structures. If training changes sensory representations, transfer (or interference) must occur since the (changed) sensory representations are common. If instead training re-weights a separate set of task connections to decision, then performance in the two tasks may still be independent. Here, we performed a co-learning analysis of two perceptual learning tasks based on identical input stimuli, following a very interesting study of Fahle and Morgan (1996) who used nearly identical input stimuli (a three dot pattern) in training bisection and vernier tasks. Two important modifications were made: (1) identical input stimuli were used in the two tasks, and (2) subjects practiced both tasks in multiple alternating blocks (800 trials/block). Two groups of subjects with counterbalanced order of training participated in the experiments. We found significant and independent learning of the two tasks. The pattern of results is consistent with the reweighting hypothesis of perceptual learning. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:25 / 32
页数:8
相关论文
共 37 条
  • [1] Linking perceptual learning with identical stimuli to imagery perceptual learning
    Grzeczkowski, Lukasz
    Tartaglia, Elisa M.
    Mast, Fred W.
    Herzog, Michael H.
    JOURNAL OF VISION, 2015, 15 (10):
  • [2] Generalization in perceptual learning across stimuli and tasks
    Kahalani-Hodedany, Ravit
    Lev, Maria
    Sagi, Dov
    Polat, Uri
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [3] Knowledge based Learning Action Analysis in Online Co-Learning
    Xu, Bin
    2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL II, PROCEEDINGS, 2008, : 162 - 166
  • [5] Employing multimodal co-learning to evaluate the robustness of sensor fusion for industry 5.0 tasks
    Rahate, Anil
    Mandaokar, Shruti
    Chandel, Pulkit
    Walambe, Rahee
    Ramanna, Sheela
    Kotecha, Ketan
    SOFT COMPUTING, 2023, 27 (07) : 4139 - 4155
  • [6] Employing multimodal co-learning to evaluate the robustness of sensor fusion for industry 5.0 tasks
    Anil Rahate
    Shruti Mandaokar
    Pulkit Chandel
    Rahee Walambe
    Sheela Ramanna
    Ketan Kotecha
    Soft Computing, 2023, 27 : 4139 - 4155
  • [7] Tutor Support Evaluation of Online Co-Learning System via Learning Action Analysis
    Xu, Bin
    2009 INTERNATIONAL SYMPOSIUM ON WEB INFORMATION SYSTEMS AND APPLICATIONS, PROCEEDINGS, 2009, : 10 - 14
  • [8] Employing Co-Learning to Evaluate the Explainability of Multimodal Sentiment Analysis
    Jain, Deepak Kumar
    Rahate, Anil
    Joshi, Gargi
    Walambe, Rahee
    Kotecha, Ketan
    IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2024, 11 (04) : 4673 - 4680
  • [9] Farmer responses to an input subsidy and co-learning program: intensification, extensification, specialization, and diversification?
    Wytze Marinus
    Gerrie WJ van de Ven
    Katrien Descheemaeker
    Bernard Vanlauwe
    Ken E Giller
    Agronomy for Sustainable Development, 2023, 43
  • [10] Farmer responses to an input subsidy and co-learning program: intensification, extensification, specialization, and diversification?
    Marinus, Wytze
    van de Ven, Gerrie W. J.
    Descheemaeker, Katrien
    Vanlauwe, Bernard
    Giller, Ken E.
    AGRONOMY FOR SUSTAINABLE DEVELOPMENT, 2023, 43 (03)